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Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide

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  • Noel Cressie

Abstract

Too much carbon dioxide (CO2) in the atmosphere is a threat to long-term sustainability of Earth's ecosystem. Atmospheric CO2 is a leading greenhouse gas that has increased to levels not seen since the middle Pliocene (approximately 3.6 million years ago). One of the US National Aeronautics Space Administration's (NASA) remote sensing missions is the Orbiting Carbon Observatory-2, whose principal science objective is to estimate the global geographic distribution of CO2 sources and sinks at Earth's surface, through time. This starts with raw radiances (Level 1), moves on to retrievals of the atmospheric state (Level 2), from which maps of gap-filled and de-noised geophysical variables and their uncertainties are made (Level 3). With the aid of a model of transport in the atmosphere, CO2 fluxes (Level 4) can be obtained from Level 2 data directly or possibly through Level 3. Decisions about how to mitigate or manage CO2 could be thought of as Level 5. Hierarchical statistical modeling is used to qualify and quantify the uncertainties at each level. Supplementary materials for this article are available online.

Suggested Citation

  • Noel Cressie, 2018. "Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 152-168, January.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:521:p:152-168
    DOI: 10.1080/01621459.2017.1419136
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    Cited by:

    1. Tingjin Chu & Jialuo Liu & Jun Zhu & Haonan Wang, 2022. "Spatio-Temporal Expanding Distance Asymptotic Framework for Locally Stationary Processes," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 689-713, August.
    2. Hang Zhang & Yong Liu & Dongyang Yang & Guanpeng Dong, 2022. "PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    3. Laura Cartwright & Andrew Zammit‐Mangion & Nicholas M. Deutscher, 2023. "Emulation of greenhouse‐gas sensitivities using variational autoencoders," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.

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